Sugar Tech paper

1 23
Sugar Tech
An International Journal of Sugar Crops
and Related Industries
ISSN 0972-1525
Volume 14
Number 3
Sugar Tech (2012) 14:237-246
DOI 10.1007/s12355-012-0166-9
Combining Ability and Heterosis over
Environments for Stalk and Sugar Related
Traits in Sweet Sorghum (Sorghum bicolor
(L.) Moench.)
A. V. Umakanth, J. V. Patil, Ch. Rani,
S. R. Gadakh, S. Siva Kumar, S. S. Rao &
Tanmay Vilol Kotasthane

1 23
Your article is protected by copyright and
all rights are held exclusively by Society for
Sugar Research & Promotion. This e-offprint
is for personal use only and shall not be self-
archived in electronic repositories. If you
wish to self-archive your work, please use the
accepted author’s version for posting to your
own website or your institution’s repository.
You may further deposit the accepted author’s
version on a funder’s repository at a funder’s
request, provided it is not made publicly
available until 12 months after publication.

RESEARCH ARTICLE
Combining Ability and Heterosis over Environments for Stalk
and Sugar Related Traits in Sweet Sorghum (Sorghum bicolor (L.)
Moench.)
A. V. Umakanth • J. V. Patil • Ch. Rani •
S. R. Gadakh • S. Siva Kumar • S. S. Rao •
Tanmay Vilol Kotasthane
Received: 18 January 2012 / Accepted: 1 June 2012 / Published online: 22 June 2012
Ó Society for Sugar Research & Promotion 2012
Abstract Till date only one sweet sorghum hybrid CSH
22SS has been released for general cultivation in India and
the current levels of new hybrids are unable to surpass this
hybrid. The objective of this study was to assess the general
and specific combining abilities of eight parents and 16
hybrids respectively at three semi arid locations by following
a line 9 tester mating design. Significant differences among
environments, testers, environments 9 testers and environ-
ments 9 line 9 tester effects were observed for all traits
suggesting the environmental influence on testers and the
interactions. The variance component estimates of specific
combining ability (SCA) were greater than that of general
combining ability (GCA) for total biomass, juice extraction
and grain yield indicating the non-additive control of genetic
variation while the GCA variance was higher than the SCA
variance for fresh stalk yield, juice yield, brix content, total
sugar yield and computed bioethanol yields indicating
additive gene action. Among females, DMS 28A for fresh
stalk, juice and grain yields and DMS 25A for brix content
were promising. Rio was a potential male parent for fresh
stalk yield, total sugar content, computed bioethanol and
grain yields. These parents can be exploited to address eth-
anol production from juice without compromising on grain
yields. The best hybrids for total biomass, fresh stalk yield,
juice yield, juice extraction, total sugar content and com-
puted bioethanol yields were DMS 13A 9 Rio and DMS
23A 9 RS 647 and after adequate testing across many
locations, these hybrids are recommended for commercial
exploitation for ethanol production.
Keywords Sweet sorghum Á Biomass Á Brix Á
Combining ability Á Ethanol
Introduction
Sorghum [Sorghum bicolor (L.) Moench] is the fifth major
cereal crop in the world and is the principal dry land coarse
cereal grown in semi-arid environments of India covering an
area of 7.53 million hectares, with a production of
7.25 million tons at a productivity of 962 kg/ha (Anony-
mous 2011). In the form of sweet sorghum, it has the capa-
bility to influence and improve the rural livelihoods in India
due to its potential industrial use for bioethanol production.
The national biofuel policy of 2009 aims at promoting bio-
fuels to meet India’s energy needs in an environmentally-
sustainable manner, while reducing its import dependence
on fossil fuels. The policy also proposed an indicative target
of 20 per cent blending of ethanol by 2017 from the current
10 per cent ethanol blending with petrol. The traditional
route of ethanol production through sugarcane molasses
would not be meeting this huge demand because of the dif-
ficulties in increasing the sugarcane area beyond the current
4.4 million ha in the country. Therefore, renewable sources
of energy in the form of other biofuel crops would be
promising options in view of the emerging trends in inter-
national energy markets as well as indigenous strengths.
Sweet sorghum has been used for nearly 150 years to pro-
duce concentrated syrup with a distinctive flavor (Schaffert
A. V. Umakanth (&) Á J. V. Patil Á Ch. Rani Á
S. S. Rao Á T. V. Kotasthane
Directorate of Sorghum Research, Rajendranagar, Hyderabad
500030, Andhra Pradesh, India
e-mail: umakanth@sorghum.res.in
S. R. Gadakh
Sorghum Research Station, MPKV, Rahuri, Maharashtra, India
S. Siva Kumar
Department of Millets, TNAU, Coimbatore, Tamil Nadu, India
123
Sugar Tech (July-September 2012) 14(3):237–246
DOI 10.1007/s12355-012-0166-9
Author's personal copy

1992). The stem juice of sweet sorghum is rich in fermen-
tative sugar and is a desirable material for alcoholic fer-
mentation. Further, the stillage from sweet sorghum after the
extraction of juice has higher biological value than the
bagasse from sugarcane when used as fodder for animals, as
it is rich in micronutrients and minerals. The sweet sorghum
bagasse is as good as the stover for the intake and body
weight gain in the animals when used as live-stock feed.
Apart from these, stillage contains similar levels of cellulose
as sugarcane bagasse, therefore has a good prospect as a raw
material for pulp product (Srinivasa Rao et al. 2009). The
bagasse or residue can also be used to cogenerate power of
about 3.2–3.4 MW/ha (Gururaj et al. 2010) for every hectare
of crop. Besides these uses, the whole plant biomass can also
be used as a substrate for production of ligno cellulosic
ethanol. Sweet sorghum was suggested as the best alternative
feedstock for bio ethanol production (Dayakar Rao et al.
2004; Shukla et al. 2006).
Sweet sorghum is the best alternative raw material to
supplement the use of sugarcane in ethanol production
according to a pilot study conducted by Vasantdada Sugar
Institute. At 5,600 l per hectare per year (from two crops,
at 70 tons per hectare of millable stalk per crop at 40 l per
ton), the ethanol production from sweet sorghum compares
well with the 6,500 l per ha per crop for sugarcane (at
85–90 tons per hectare of millable cane per crop at 75 l per
ton) (Anonymous 2004). Techno-economic feasibility
studies have shown that the cost of alcohol production from
sweet sorghum was Rs 1.87 less than that from molasses.
This conclusion was based on the prevailing prices of
molasses during that period. In addition to sweet stalk, an
average grain yield of 1.5–2.0 t/ha can be harvested which
can be used as food or feed (Dayakar Rao et al. 2004).
Concerted research efforts during last two decades at
Directorate of Sorghum Research and its cooperating centres
in different State Agricultural Universities under National
Agricultural Research System and at ICRISAT have resulted
in excellent sweet sorghum varieties for use in ethanol pro-
duction by the sugar industries/alcohol distilleries and for
use as green/dry fodder. However, till date only one sweet
sorghum hybrid CSH 22SS has been released (in the year
2005) for general cultivation in India and the current yield
levels of new hybrids are unable to surpass this hybrid. This
necessitates the identification of new hybrid parents with
good combining ability for different traits of interest.
In hybrid oriented breeding programmes, the knowledge
of combining ability of the parents and the inheritance of
the traits is important (Itai et al. 2010). This information
helps in optimizing the breeding strategy, either selection
when general combining ability (GCA) effects are impor-
tant; inbreeding followed by hybrid breeding when specific
combining ability (SCA) effects are predominant; or
selection followed by hybridization if both are important;
because GCA effects are attributed to preponderance of
genes with additive effects and SCA indicates predomi-
nance of genes with non-additive effects (Kenga et al.
2004; Mutengwa et al. 1999). Studies have shown both
GCA and SCA to be important in many sorghum traits
including grain yield (Haussmann et al. 1999; Tadesse
et al. 2008; Yu and Tuinstra 2001). The objective of this
study was to determine the combining ability of 8 parents
for stalk and sugar related traits. The study envisaged
assessing the general combining ability of parents and
specific combining ability of hybrids by following a
line 9 tester (L 9 T) mating design and the experiment
was conducted at 3 diverse locations as significant geno-
type 9 environment interaction effects have been reported
in sorghum (Chapman et al. 2000).
Materials and Methods
Plant Material
Four cytoplasmic genetic male sterile lines (DMS 13A, DMS
23A, DMS 25A and DMS 28A) used as females (A-lines)
were crossed on to each of the four male-fertile lines (N 98,
Rio, RSCN 5008 and RS 647) in line 9 tester fashion to
produce 16 F1 hybrids during post rainy season of 2009 at
Directorate of Sorghum Research (DSR), Hyderabad. The
males included two sweet stalked temperate lines (N 98 and
Rio) and two sub-tropical breeding derivatives (RSCN 5008
and RS 647) in sweet stalk background with high stalk yields
while the females had higher sugar content (Table 1). The
parental line selection criteria were based on characters
contributing to increased stalk and sugar yields.
Experimental Sites
The 16 F1 hybrids, their corresponding eight parents and
three checks were evaluated at three different locations in
three different states of India with semi-arid environments
viz., Directorate of Sorghum Research (DSR) farm, Hy-
derabad, Andhra Pradesh (latitude 17°190
N, longitude
78°230
E), Centre for Plant Breeding and Genetics farm,
Tamil Nadu Agricultural University, Coimbatore, Tamil
Nadu (latitude 11°00
N, longitude 76°550
E) and Sorghum
Research Station farm, Mahatma Phule Krishi Vidyapeeth,
Rahuri, Maharashtra (latitude 19°200
N, longitude 74°380
E)
during rainy season of 2010. The experiment was con-
ducted in a Randomized Block Design with three replica-
tions at all the three locations. Each entry was raised in two
rows of 4 m length with 60 9 15 cm spacing. Recom-
mended agronomic practices were followed throughout the
crop season. Atrazine (1.0 kg/ha of active ingredient) was
applied immediately after sowing. A basal fertilizer dose of
238 Sugar Tech (July-September 2012) 14(3):237–246
123

42 kg/ha N, 42 kg/ha P2O5 was applied just before sow-
ing, in the 2nd week of June 2010, and a topdressing of
46 kg/ha N was applied 1 month after germination (floral
initiation stage) in the third week of July 2010. In each rep-
lication, observations were recorded on 10 randomly selec-
ted competitive plants. At physiological maturity, data was
recorded on following traits.
Traits Studied
Total fresh biomass (t/ha): At physiological maturity, all
the plants in net plot with their leaves, stems, and panicles
were weighed in kilograms and then converted into tons
per hectare.
Fresh stalk yield (t/ha): The leaves plus sheath and pan-
icles were removed from the plants carefully and the weight
of the fresh (millable) stalk yield in the net plot was recorded
in kilograms and later converted into tons per hectare.
Juice yield: The extraction of juice from 10 randomly
selected plants was done on an electrically operated three-
roller stalk crusher with a minimum of three passings of the
fresh (millable) stalk so that the last drop of juice came out
from the stems. Juice extracted was measured in kilograms
and later converted into litres per hectare.
Brix content: It is a measure of the mass ratio of soluble
solids to water, is a widely used approximation for sugar
content and is reported as a trait in the rest of the text. The
brix values from the composite juice were recorded °brix,
using an Atago PAL-1 digital hand-held pocket refrac-
tometer (with automatic temperature compensation ranging
from 0 to 50 °C) at the hard dough stage.
Juice extraction (%): The juice extractability in percent
was calculated using the data of total weight of ten fresh
stalks
Juice extraction %ð Þ ¼ Juice weight=Fresh stalk yieldð Þ½
Ã100Š:
Total Sugar yield (t/ha): Total sugar yield was calculated
on the basis of following formula (Reddy et al. 2005).
Total soluble sugar %ð Þ=100ð Þ Â Juice yield kl=hað Þ
Grain yield: All the panicles were collected from the net
plot, threshed, dried and the dry weight was recorded in
kilograms at 14 % seed moisture content and later
converted into kilograms per hectare.
Computed ethanol yields: The ethanol yields were cal-
culated based on the total sugar yield (Smith and Buxton
1993).
Statistical Analysis
Analysis of variance for combining ability was carried out
using mean values across environments (Kempthorne 1957)
to test the significance of differences among the genotypes
including crosses and parents (Snedecor and Cocharan 1967;
Panse and Sakhatme 1964). The sum of squares for hybrids
was further partitioned into variation due to lines, testers and
line 9 tester interactions. The mean squares due to lines and
testers were tested against the mean squares due to
line 9 tester, and the latter were tested against the pooled
error. The mean squares due to environment 9 line and
environment 9 tester were tested against the mean squares
due to environment 9 tester 9 line, and the latter was tested
against the pooled error. Estimate of GCA variances
(r2
GCA) and SCA variances (r2
SCA) were obtained (Singh
and Chaudhary 1977). Mid-parent heterosis and better parent
heterosis were estimated and tested by working out the
standard errors (Hays et al. 1955).
Results
Combined analyses of variance for eight characters mea-
sured over three environments are presented in Table 2.
Significant differences among environments, testers, envi-
ronments 9 testers and environments 9 line 9 tester
effects were observed for all the characters studied sug-
gesting that the testers and the interactions for these traits
were influenced by the environment. The line 9 tester
Table 1 Details of parental sorghum lines used to generate 16 hybrids
Line no Name Fertility status Origin Role in crosses Principal selection criteria
1 DMS 13A CMS India Female High sugar, high stalk yield
2 DMS 23A CMS India Female High sugar, high stalk yield
3 DMS 25A CMS India Female High sugar
4 DMS 28A CMS India Female High sugar
5 N 98 CMF USA Male High sugar
6 Rio CMF USA Male High sugar, high stalk yield
7 RSCN 5008 CMF India Male High sugar, high stalk yield
8 RS 647 CMF India Male High sugar, high stalk yield
CMF cytoplasmic male fertile, CMS cytoplasmic male sterile
Sugar Tech (July-September 2012) 14(3):237–246 239
123

effect was also significant for all the traits except brix
content indicating the existence of genetic diversity in the
material tested. The line effects were non-significant for
total biomass while the environment 9 line effect was not
significant for total sugar content, grain yield and com-
puted bioethanol yields.
The importance of the source of variation is indicated by
the relative magnitude of variance components. The vari-
ance component estimates of SCA were greater than that of
GCA for total biomass, juice extraction and grain yield
(Table 3) indicating the non-additive control of genetic
variation for these traits. On the contrary, the GCA vari-
ance was higher than the SCA variance for fresh stalk
yield, juice yield, brix content, total sugar yield and com-
puted bioethanol yields indicating the presence of additive
gene action. In addition, the ratio of the mean square
components associated with variance of GCA and SCA
was in negative direction and much less than the theoretical
maximum of unity for most of the traits studied. There was
significant interaction of variance due to SCA with envi-
ronment for all the characters studied except juice extrac-
tion (%).
The selection of parental lines for hybrid programs was
the main objectives of this study. Thus, the estimates of the
general combining ability (gi) of a parent provide impor-
tant indicators of its potential for generating superior lines.
A low gi estimate, whether positive or negative, indicates
that the mean of a parent in crossing with the other, does
not differ greatly from the general mean of the crosses. On
the other hand, a high gi estimate indicates that the parental
mean is superior or inferior to the general mean (Kenga
et al. 2004).
Estimates of GCA effects for different traits viz., total
biomass, fresh stalk yield, juice yield, brix content, juice
extraction, total sugar content, grain yield and computed
bioethanol yields for the eight parents used in this study are
presented in Table 4. DMS 28A was the most promising
female parent for most of the traits like fresh stalk yield,
juice yield, juice extraction and grain yield with highly
significant and positive GCA effects but the GCA effects
for brix content were in the negative direction. The female
DMS 23A was the best general combiner for total biomass
while DMS 25 A was good general combiner for total
biomass, brix content and juice extraction. On the other
hand, the female DMS 13A showed significant and nega-
tive GCA effects for total biomass, juice yield, juice
extraction and grain yield.
Among the male parents (testers), N 98 was the best
general combiner for juice extraction. However it was not a
good combiner for total biomass, fresh stalk and grain
yields. Rio was the best combiner for most of the important
traits related to biofuel production like fresh stalk yield,
brix content, total sugar content, grain yield and computed
Table2Pooledanalysisofvarianceforcombiningabilityinline9testeranalysisofsweetsorghum
SourceDegree
offreedom
Totalbiomass
(t/ha)
Freshstalkyield
(t/ha)
Juiceyield(l/ha)Brixcontent
(%)
Juiceextraction
(%)
Totalsugar
yield(t/ha)
Grainyield
(kg/ha)
Computedethanol
yields(l/ha)
Environments223,083.50***8,072.28***559,950,080.00***1,098.26***3,337.04***13.54***14,414,082.00***3,799,307.75***
Parents(line)310.41119.44***21,784,970.00***14.70***48.27***0.37***1,467,017.88**103,104.55***
Parents(testers)3436.77***116.18***59,796,116.00***84.78***797.57***1.59***2,451,725.25***440,501.72***
Line9testereffect9355.10***117.37***7,493,865.00**4.39125.42***0.32***2,980,356.25***81,521.52***
Env9parents(L)686.07***59.33***7,247,019.50**6.45*18.50***0.09408,080.2821,093.05
Env9parents(T)6238.49***69.29***21,139,448.00***33.68***236.18***0.27***1,174,779.25***75,233.90***
Env9L9Teffect18215.87***32.87***9.06***11.01***0.33***17,732,490***93,143.59***517,586.50*
Error13815.272.161.182.410.052,284,184.5013,742.74286,545.00
*,**,***SignificantatPB0.05,0.01and0.001,respectively
123

bioethanol yields. The testers RSCN 5008 and RS 647 were
promising for total biomass only.
It was observed that on a pooled basis, significant and
positive SCA effects for total biomass were shown by 5
hybrids viz., DMS 23A 9 RS 647, DMS 13A 9 Rio, DMS
13A 9 RSCN 5008, DMS 25A 9 N 98 and DMS
25A 9 Rio. These hybrids showing significant and positive
SCA effects were also among the best in per se perfor-
mance (Table 5). With respect to fresh stalk yield, DMS
13A 9 Rio and DMS 23A 9 RS 647 exhibited significant
and positive SCA effects and the trend in per se perfor-
mance was similar to total biomass. However, the hybrid
DMS 28A 9 Rio which was the top fresh stalk yielder
showed insignificant negative effects. For juice yield, the
hybrid DMS13A 9 Rio recorded significant and positive
SCA effects and it also excelled in biomass traits too. DMS
28A 9 Rio and DMS 28A 9 RSCN 5008 were the other
hybrids with high mean juice yields but insignificant and
positive SCA effects. Out of 16 hybrids, only one hybrid
DMS 13A 9 RSCN 5008 demonstrated significant and
positive SCA effect for brix content. DMS 25A 9 Rio
recorded the highest brix content in the trial but had shown
a non-significant SCA effect which was in the positive
direction. Six hybrids viz., DMS 23A 9 N 98, DMS
25A 9 RSCN 5008, DMS 28A 9 N 98, DMS 13A 9 Rio,
DMS 25A 9 Rio and DMS 25A 9 RS 647 showed
desirable SCA effects for juice extraction. All these six
hybrids recorded higher juice yields. However it was
gratifying to note that three hybrids exhibited positive SCA
effects while the other three have shown negative SCA
effects for juice yields which were non-significant. For
total sugar content, the hybrids DMS 13A 9 Rio and DMS
Table 3 Estimates of variance components as reference to the prevailing gene action
Source Total
biomass
(t/ha)
Fresh stalk
yield (t/ha)
Juice yield (l/ha) Brix
content
(%)
Juice
extraction
(%)
Total sugar
yield (t/ha)
Grain yield
(kg/ha)
Computed
ethanol
yields (l/ha)
r2
Environments 221.788** 94.873*** 9,905,079.170*** 13.693*** 34.368** 0.215*** 133,643.140 ** 60,204.159***
r2
gca -10.982 1.301 227,523.945 0.329 4.624 0.001 60,166.904 643.923
r2
sca 15.469 -3.412 -1,137,624.929 -0.736 10.283** -0.001 273,641.090 *** -1,291.342
r2
gca/r2
sca -0.709 -0.381 -0.200 -0.447 0.449 -1.666 0.219 -0.498
r2
gca 9 environments
50.998* -0.273 -305,541.301 -0.429 8.408 -0.000 9,403.367 -441.428
r2
sca 9 environments 66.718*** 45.969*** 5,083,964.079*** 2.985*** 10.092 0.090*** 84,664.377 * 26,077.082 ***
*, **, *** Significant at P B 0.05, 0.01 and 0.001, respectively
Table 4 Pooled estimates of general combining ability effects of parents in sweet sorghum
S. No. Parents Total
biomass (t/ha)
Fresh stalk
yield (t/ha)
Juice yield
(l/ha)
Brix
content (%)
Juice
extraction
(%)
Total sugar
yield (t/ha)
Grain yield
(kg/ha)
Computed ethanol
yields (l/ha)
Females
1. DMS 13A -5.278*** 0.035 -566.174* 0.092 -5.376*** -0.029 -414.896*** -16.333
2. DMS 23A 3.028*** -1.354* -421.229 -0.322 -0.234 -0.082* -157.785 -44.083*
3. DMS 25A 2.806*** -0.326 121.993 0.544* 3.858*** 0.043 104.521 20.278
4. DMS 28A -0.556 1.646** 865.410** -0.314 1.752*** 0.068 468.160*** 40.139
S.E (gi) 0.660 0.0531 262.498 0.239 0.268 0.039 85.568 20.352
S.E (gi-gj) 0.934 0.751 371.22 0.33 0.379 0.055 121.012 28.78
Males
1. N 98 -5.472*** -2.826 *** -861.104 0.328 3.722*** -0.043 -408.285*** -22.889
2. Rio -0.778 3.618*** 33.868 0.669 ** -1.481*** 0.188*** 454.882*** 95.278***
3. RSCN 5008 3.139*** -1.021 826.785 -0.119 -1.190*** -0.06 169.965 -28.667
4. RS 647 3.111*** 0.229 0.451 -0.878 *** -1.051*** -0.085* -216.563* -43.722*
S.E (gi) 0.660 0.053 262.498 0.239 0.268 0.039 85.568 20.352
S.E (gi-gj) 0.934 0.751 371.22 0.33 0.379 0.055 121.012 28.78
*, **, *** Significant at P B 0.05, 0.01 and 0.001, respectively
123

Table5Estimatesofspeciﬁccombiningabilityeffectsandmeansofcrossesinsweetsorghum
S.
No.
CrossesTotal
biomass
(t/ha)
MeanFreshStalk
Yield(t/ha)
MeanJuiceyield
(l/ha)
MeanBrix(%)MeanJuice
extraction
(%)
MeanTotalsugar
yield(t/ha)
MeanGrainyield
(kg/ha)
MeanComputed
ethanolyields
(l/ha)
Mean
1DMS13A9N
98
2.55632-0.25723237.1467,555.770.50815.6-0.308320.0320.95404.535*2,63810.444502
2DMS
13A9Rio
6.917***413.521***281,132.563*9,617.77-0.52214.91.451**290.335***1.49-844.410***2,252171.167***781
3DMS
13A9RSCN
5008
6.611***450.16025-420.0767,215.331.011*15.7-1.330*26-0.0850.82102.7292,915-38.000447
4DMS13A9RS
647
-1.25037-5.424***21-949.6326,729.66-0.997*12.90.18728-0.282***0.6337.1462,763-143.611***327
5DMS23A9N
98
-5.528***321.13223818.2018,281.77-0.10014.65.062***420.0180.8919.3132,51013.750477
6DMS
23A9Rio
0.66743-5.090***23-873.8267,756.330.42515.4-3.602***29-0.157*0.94493.035**3,847-77.861504
7DMS
23A9RSCN
5008
-3.139*43-0.45123-926.4656,853.88-0.55313.7-0.32732-0.0760.78-255.2712,814-41.361416
8DMS23A9RS
647
8.000***544.410***29982.0908,806.330.22813.7-1.133*320.215**1.04-257.0762,426105.472*548
9DMS25A9N
98
5.694***430.88223-194.2437,812.55-0.54415-6.852***350.0041-84.1042,669-2.389525
10DMS
25A9Rio
5.111***470.54930-292.6048,880.770.32516.21.373*38-0.0931.13-511.826**3,105-42.556603
11DMS
25A9RSCN
5008
-5.583***41-1.25723519.7578,843.330.12515.24.359***410.1101.08547.868**3,87955.278577
12DMS25A9RS
647
-5.222***41-0.17425-32.9108,334.550.09414.41.120*38-0.0210.9348.0632,993-10.333497
13DMS28A9N
98
-2.722*31-1.75723-861.1047,889.110.13614.82.098***41-0.0540.97-339.7432,777-21.806526
14DMS
28A9Rio
2.13941-0.9793033.8689,950.66-0.22814.80.77835-0.0851.17863.201***4,843-50.750615
15DMS
28A9RSCN
5008
2.111451.54928826.7859,893.77-0.58313.7-2.702***320.0511.06-395.326*3,30024.083566
16DMS28A9RS
647
-1.528411.188290.4519,111.330.67514.2-0.174340.0871.07-128.1323,18148.472575
S.Em±1.3211.063524.9970.4770.5360.078171.13740.705
*,**,***SigniﬁcantatPB0.05,0.01and0.001,respectively
123

23A 9 RS 647 showed significant and positive SCA
effects apart from higher mean sugar contents. DMS
13A 9 Rio and DMS 23A 9 RS 647 were the only crosses
to exhibit significant and positive SCA effects for com-
puted bioethanol yields. However, the hybrids DMS
28A 9 Rio and DMS 25A 9 Rio were among the top
mean ethanol yielders though they have recorded insig-
nificant and negative SCA effects. For grain yield, DMS
28A 9 Rio, DMS 25A 9 RSCN 5008 and DMS
23A 9 Rio displayed significant SCA effects in positive
direction.
Heterosis
Mid-parental heterosis and better parental heterosis for
important traits like total biomass, juice yield, brix content
and computed ethanol yield were studied.
Mid-parental heterosis
The mid-parental heterosis for total biomass, juice yields
and computed ethanol yields are depicted in Fig. 1. For
total biomass and juice yields, 11 out of 16 hybrids
exhibited significant and positive heterosis. The hybrid
DMS 23A 9 RS 647 recorded 62 % heterosis for total
biomass while DMS 23A 9 N98 followed by DMS
13A 9 N 98 exhibited 79 and 69 % heterosis respectively
for juice yield. With respect to brix content, only one
hybrid DMS 28A 9 RS 647 has shown significant heter-
osis up to 9 % while five hybrids recorded significant
heterosis in negative direction. Stem sugar heterosis values
up to 7.39 % in Texas, USA were reported (Corn 2008).
Significant and positive heterosis for computed ethanol
yields ranged from 26 % in DMS 25A 9 RS 647–98 % in
DMS 23A 9 RS 647.
Better parent heterosis
Six hybrids have shown significant and positive better
parent heterosis ranging between 12 to 41 % for total
biomass. The hybrid DMS 23A 9 RS 647 which exhibited
significant mid parent heterosis for total biomass also
registered significant heterosis (41 %) in positive direction
(Table 6). For juice yield, seven out of 16 hybrids have
shown significant and positive better parent heterosis. The
hybrid DMS 28A 9 RSCN 5008 registered 60 % signifi-
cant heterosis. For brix content, 12 hybrids exhibited sig-
nificant heterosis in negative direction. Better parent
heterosis for stem brix up to 45 % was observed in hybrids
(Itai et al. 2009). Eight out of 16 hybrids have shown
significant and positive better parent heterosis for
computed bioethanol yields which ranged between 19 and
86 %. The hybrids viz., DMS 23A 9 RS 647 (86 %), DMS
28A 9 RS 647 (69 %), DMS 23A 9 N98 and DMS
28A 9 RSCN 5008 (62 %) have shown more than 60 %
better parent heterosis for this trait.
Fig. 1 Mid-parent heterosis for a total biomass, b. juice yield and
c computed ethanol yields in sweet sorghum hybrids
123

Discussion
The mean squares due to environment, entries and their
interactions were significant indicating the genotypic
diversity and their responses to environment. Partitioning
of the mean squares into variations attributable to testers
and line 9 testers showed that variation within each group
with environment was significant for most of the traits.
Similar to the present investigations, in a study on com-
bining ability and heterosis in sweet sorghum germplasm,
significant site 9 hybrid interaction effects for stem brix,
stem biomass weight ha-1
and stem brix-juice index were
reported (Itai et al. 2009). In a study on tropical sorghums
across four environments, significant mean squares due to
environment, entries and environment-entries interactions
were observed (Kenga et al. 2004). Testers were more
variable than lines in a line 9 tester study across locations
and years in forage sorghum (Mohammed and Mohamed
2009) and in sweet sorghum (Indhubala et al. 2010) similar
to the present study. The significance of line 9 tester effect
for most of the traits except brix content suggests the
presence of high heterotic responses for these traits. Mean
stem °brix and stem °brix-juice index were not significantly
(P B 0.05) different between the parents, hybrids and the
standard check variety in a study on combining ability and
heterosis of sorghum germplasm for stem sugar traits under
off-season conditions in tropical lowland environments
(Itai et al. 2009) which is similar to the present findings.
The estimates of variances due to combining ability
revealed the significance of both additive (fresh stalk yield,
juice yield, brix content) and the non-additive type of gene
actions (total biomass and grain yield) for important traits.
This implies that improvement for these traits can be
achieved through both selection and hybridization. More-
over a definitive separation of additive, dominance and
non-additive genetic effects for these traits requires eval-
uation of additional sets of genetic material. The total
biomass yields among the hybrids ranged from 26 to 54 t/
ha in the present investigation and this trait is of paramount
importance in breeding sweet sorghum for biofuel pro-
duction. Similar to the present study, stem biomass yields
of 47.9, 46.4 and 39.5 t ha-1
for cultivars Wray, Keller and
Rio, respectively were reported from in-season evaluations
in Indonesia (Tsuchihashi and Goto 2004). Heterosis
breeding could be exploited for increasing the biomass
yields owing to the importance of non-additive gene action
in determining this character. The other important biofuel
trait, i.e., brix content ranged from 12.9° to 16.2° in the
hybrids and was controlled by additive gene action and
further gains for this trait can be achieved through selec-
tion. Earlier studies (Tsuchihashi and Goto 2004; Woods
2000) have demonstrated stem sugar concentrations of
between 14.0 and 18.5° brix with specialized sweet sor-
ghum cultivars. Stem °brix values of about 13 under dry-
land production were reported in Indonesia (Tsuchihashi
and Goto 2004). The interaction effect of SCA variance
with that of environment was significant for most of the
characters studied. The predominant role of non-additive
gene action for plant height, stem girth, total soluble solids,
millable sweet-stalk yield and extractable juice yield was
Table 6 Better parent heterosis in 16 sweet sorghum hybrids
Hybrid Total biomass (tons/ha) Juice yields (l/ha) Brix (%) Computed ethanol yields (l/ha)
DMS 13A 9 N 98 -16.52** 42.14** -10.69** 54.62**
DMS 13A 9 Rio -34.81** -2.34 -18.02** 6.33
DMS 13A 9 RSCN 5008 16.52** 24.92** 1.95 28.25*
DMS 13A 9 RS 647 -4.06 2.72 -16.00** 0.72
DMS 23A 9 N 98 -16.18** 48.02** -16.54** 62.32**
DMS 23A 9 Rio 7.18 -21.24** -15.09** -31.38**
DMS 23A 9 RSCN 5008 12.43* 18.66 -10.93* 19.33*
DMS 23A 9 RS 647 41.33** 34.42** -0.48 86.43**
DMS 25A 9 N 98 15.09** -10.46 -14.12** -0.90
DMS 25A 9 Rio 17.68** -9.82 -10.87** -17.80*
DMS 25A 9 RSCN 5008 7.99 1.35 -9.09* 8.89
DMS 25A 9 RS 647 8.88 -4.48 -13.80** -6.33
DMS 28A 9 N 98 -21.61** 27.42* -15.14** 54.42**
DMS 28A 9 Rio 1.93 1.04 -18.63** -16.21*
DMS 28A 9 RSCN 5008 11.91* 59.80** -11.07* 62.23**
DMS 28A 9 RS 647 2.77 39.08** -2.38 68.94**
*, ** Significant at P B 0.05 and 0.01, respectively
123

observed in a study on heterosis and combining ability for
juice yield related characteristics in sweet sorghum
(Sankarapandian et al. 1994) indicating the importance of
heterosis breeding for improving these traits.
The identification of new hybrid parents with good
combining ability for different traits of interest was one of
the important objectives of the present study. For total
biomass, DMS 23A and DMS 25A among female parents
and RSCN 5008 and RS 647 among males are potential
parents for improving the total biomass as these parents
exhibited high and significant GCA values. These parents
can also be utilized for development of feedstock material
for the production of 2nd generation biofuels apart from
their use for production of hybrids to address biofuel from
sweet sorghum juice. For fresh stalk yield, DMS 28A from
female group and Rio among males were promising while
the former one was a good combiner for juice yield and
grain yield also and these can be used in hybrid making to
address ethanol production from juice without compro-
mising on grain yields. For brix content, DMS 25A among
female parents and Rio among males were potential parents
and thus can be exploited for improving the brix content. It
was gratifying to note that Rio had also exhibited signifi-
cant and positive GCA effects for total sugar content,
computed bioethanol and grain yields. It was observed that
for various characters, high combiners were the male
parents.
In this study, the hybrids DMS 13A 9 Rio (for total
biomass, fresh stalk yield, juice yield, juice extraction, total
sugar content and computed bioethanol yields), DMS
23A 9 RS 647 (for total biomass, fresh stalk yield, total
sugar content and computed bioethanol yields), DMS
13A 9 RSCN 5008 (for total biomass and brix content),
DMS 13A 9 N 98, DMS 23A 9 Rio, DMS 25A 9 RSCN
5008 and DMS 28A 9 Rio (for grain yield) exhibited
significantly higher SCA effects. It is evident that most of
the hybrids promising for various traits were observed to be
constituted from hybrids with both or one parent exhibiting
significant GCA effects and produced hybrids with higher
SCA effects. The significance of both GCA and SCA
effects suggesting both additive and non-additive gene
effects for grain yield was observed (Itai et al. 2009).
The heterosis levels observed in this study could also
explain the high biomass, juice and computed ethanol
yields observed for hybrids as compared to the parents.
Most of the hybrids which recorded significantly positive
heterosis also recorded higher SCA effects.
Parental selection for crop improvement programmes
cannot be based on SCA effects alone, but in association
with hybrid means and GCA effects of the parents involved
(Marilia et al. 2001). It is prudent to consider only those
hybrids between parents with positive and significant GCA
effects because genetic gain is realized in the presence of
sufficient additive variances. The interaction effect of SCA
variance with that of environment was significant for all the
characters except juice extraction. This implies that the
environment significantly influenced the expression of non-
additive gene effects. The observation of significant envi-
ronmental influences on SCA effects is consistent with
reports that genotype 9 environment interaction is impor-
tant in sorghum (Panse and Sakhatme 1964; Chapman et al.
2000; Yu and Tuinstra 2001). Therefore, it is necessary to
conduct multi-location testing for GCA and SCA to select
the best parents and potential hybrids (Itai et al. 2010)
before deploying specific sweet sorghum hybrids in dif-
ferent environments for commercial cultivation which
ultimately benefit the poor farmers of the semi-arid tropics.
Conclusions
The parents viz., DMS 28A among females for fresh stalk,
juice and grain yields, Rio among males for fresh stalk
yield were identified as promising for biomass traits as they
have shown positive and significant GCA and in combi-
nation large SCA effects. For quality traits, DMS 25A
among female parents and Rio among males were found to
be promising as potential parents for brix content while the
latter had also exhibited significant and positive GCA
effects for total sugar yield and computed bioethanol
yields. All these parents can be used in sweet sorghum
cultivar development programs to address ethanol pro-
duction from juice without compromising on grain yields
and offer solution to the ongoing food vs fuel debate. The
study also demonstrated the significance of both additive
and the non-additive type of gene actions for important
traits. Most of the hybrids which recorded significantly
positive heterosis also recorded higher SCA effects. Fur-
ther the study identified the hybrids DMS 13A 9 Rio and
DMS 23A 9 RS 647 with significantly higher SCA effects
for sweet sorghum productivity traits. These hybrids would
be recommended for further testing across many locations
in the semi-arid target production environments for ethanol
production.
Acknowledgments Authors are grateful to the Indian Council of
Agricultural Research for financial support and the staff at Rahuri and
Coimbatore locations for their assistance in running the sweet sor-
ghum trials.
References
Anonymous. 2004. ICRISAT to promote sweet sorghum for produc-
tion of ethanol. Business Line; 2004 Aug 13; Sect. Industry and
Economy.
Anonymous. 2011. Food and Agricultural Organization: FAO statis-
tics database on the World Wide Web. Available at:
123

http://apps.fao.org/default.jsp and http://faostat.fao.org. Last
accessed on 9 Dec 2011.
Chapman, S.C., M. Cooper, D.G. Butler DG, and R.G. Henzell. 2000.
Genotype by environment interactions affecting grain sorghum.
I. Characteristics that confound interpretation of hybrid yield.
Australian Journal of Agricultural Research 51: 197–207.
Corn, R. 2008. Sweet sorghum heterosis. Joint annual meeting, 5–9
October, Houston, TX, George R. Brown Convention Centre.
Available online at http://a-c-s.confex.com/crops/2008am/web
program/Paper42644.html. Last accessed on 1 December 2011.
Dayakar Rao, B., C.V. Ratnavathi, K. Karthikeyan, P.K. Biswas, S.S.
Rao, Vijaya Kumar, and N. Seetharama. 2004. Sweet sorghum
cane for biofuel production: A SWOT analysis in Indian context.
NRCS—National Research Centre for Sorghum, Rajendranagar,
Hyderabad 500 030, AP, India. Technical report no. 21.
Gururaj, H., N.R. Yekkeli, and B.Y. Kongawad. 2010. Sweet stalk
sorghum: an alternative sugar crop for ethanol production. Sugar
Tech 12(1): 79–80.
Haussmann, B.I.G., A.B. Obilana, P.O. Ayiecho, A. Blum, W.
Schipprack, and H.H. Geiger. 1999. Quantitative-genetic param-
eters of sorghum [Sorghum bicolor (L.) Moench] grown in semi-
arid areas of Kenya. Euphytica 105: 109–118.
Hays, H.K., I.R. Immer, and D.C. Smith. 1955. Heterosis in methods
of Plant Breeding, 52–65. New York: Mcgraw-Hill Book
Company Inc.
Indhubala, M., K. Ganesamurthy, and D. Punitha. 2010. Combining
ability studies for quality traits in sweet sorghum (Sorghum
bicolor (L.) Moench). Madras Agricultural Journal 97(1–3):
17–20.
Itai, M., T. Pangirayi, D. John, S. Julia, and F. Pedro. 2010.
Combining ability and cultivar superiority of sorghum germ-
plasm for grain yield across tropical low- and mid-altitude
environments. Field Crops Research 116: 75–85.
Itai, M., T. Pangirayi, and John. Derera. 2009. Combining ability and
heterosis of sorghum germplasm for stem sugar traits under off-
season conditions in tropical lowland environments. Field Crops
Research 114: 272–279.
Kempthorne, O. 1957. An introduction to genetical statistics. New
York: Willey.
Kenga, R., S.O. Alabi, and S.C. Gupta. 2004. Combining ability
studies in tropical sorghum [Sorghum bicolor (L.) Moench].
Field Crops Research 88: 251–260.
Mohammed, Maarouf I., and Moataz A. Mohamed. 2009. Evaluation
of newly developed sweet sorghum (Sorghum bicolor) genotypes
for some forage attributes. American-Eurasian Journal of
Agricultural and Environmental Sciences 6(4): 434–440.
Marilia, C.F., T.C. Servio, O.R. Valter, V. Clibas, and T.M. Siu. 2001.
Combining ability for nodulation in common bean (Phaseolus
vulgaris L.) genotypes from Andean and Middle American gene
pools. Euphytica 118: 265–270.
Mutengwa, C.S., P. Tongoona, S. Mabasa, and O.A. Chivinge. 1999.
Resistance to Striga asiatica (L.) Kuntze in sorghum: Parent
characterization and combining ability analysis. African Crop
Science Journal 7: 321–326.
Panse, V.G., and P.V. Sakhatme. 1964. Statistical methods for
agricultural workers, 2nd ed. New Delhi: ICAR.
Reddy, B.V.S., S. Ramesh, P. Sanjana Reddy, B. Ramaiah, P.M.
Salimath, and Rajashekar. Kachapur. 2005. Sweet sorghum—A
potential alternative raw material for bioethanol and bio-energy.
International Sorghum and Millets Newsletter 46: 79–86.
Sankarapandian, R., J. Ramalingam, M.A. Pillai, and C. Vanniarajan.
1994. Heterosis and combining ability studies for juice yield
related characteristics in sweet sorghum. Annals of Agricultural
Research 15(2): 199–204.
Schaffert, R.E. 1992. Sweet sorghum substrate for industrial alcohol.
In Utilization of sorghum and millets. Proceedings of the
International workshop on policy, practice, and potential
relating to uses of sorghum and millets; 1988 Feb 8–12;
ICRISAT Center, Bulawayo, Zimbabwe, eds. Gomez, M.I.,
House, L.R., Rooney, L.W., Dendy, D.A.V., 131–137. Interna-
tional Crops Research Institute for the Semi-Arid Tropics.
Shukla, G.K., S.K. Gupta, S. Lakhendra, S.S. Rao, C.V. Ratnavathi,
and B. Dayakar Rao. 2006. Successful pilot production of bio-
ethanol from sweet sorghum in sub-tropical north India. Jowar
Samachar 2(1): 1.
Singh, R.K., and B.D. Chaudhary. 1977. Biometrical methods in
quantitative genetic analysis. Ludhiana: Kalyani Publishers.
Smith, G.A., and Buxton. 1993. Temperate zone sweet sorghum
ethanol production potential. Bioresource Technology 43: 71–75.
Snedecor, G.W., and W.G. Cocharan. 1967. Statistical methods. New
Delhi: Oxford IBH.
Srinivasa Rao, P., S.S. Rao, N. Seetharama, A.V. Umakanth, P.
Sanjana, B.V.S. Reddy, and C.L.L. Gowda (eds). 2009. Sweet
sorghum for biofuel and strategies for its improvement. ICRI-
SAT information bulletin No. 77.
Tadesse, T., T. Tesso, and G. Ejeta. 2008. Combining ability of
introduced sorghum parental lines for major morpho-agronomic
traits. SAT eJournal 6: 1–7.
Tsuchihashi, N., and Y. Goto. 2004. Cultivation of sweet sorghum
(Sorghum bicolor (L.) Moench) and determination of its harvest
time to make use as the raw material for fermentation, practiced
during rainy season in dry land Indonesia. Plant Production
Science 7: 442–448.
Woods, J. 2000. Integrating sweet sorghum and sugarcane for
bioenergy: Modeling the potential of electricity and ethanol
production in SE Zimbabwe. Ph.D. Thesis. Kings College
London.
Yu, J., and M.R. Tuinstra. 2001. Genetic analysis of seedling growth
under cold temperature stress in grain sorghum. Crop Science
41: 1438–1443.
123

Sugar Tech paper

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

Destaque

Destaque (20)

Semelhante a Sugar Tech paper

Semelhante a Sugar Tech paper (20)

Sugar Tech paper